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Industrial and Medical Anomaly Detection Through Cycle-Consistent Adversarial Networks
The model would be able to identify its weaknesses by better learning how to transform an abnormal (respectively normal) image into a normal (respectively abnormal) one, helping the entire model to learn better than a single normal to normal reconstruction.
Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset
Nevertheless, several challenges have to be faced, including imbalanced datasets, the image complexity, and the zero-false-negative (ZFN) constraint to guarantee the high-quality requirement.
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